There are two key primary ways of scaling web applications which is in practice today.1) “Vertical Scalability” - Adding resource within the same logical unit to increase capacity. An example of this would be to add CPUs to an existing server, or expanding storage by adding hard drive on an existing RAID/SAN storage.2) “Horizontal Scalability” - Adding multiple logical units of resources and making them work as a single unit. Most clustering solutions, distributed file systems, load-balancers help you with horizontal scalability.

Scalability can be further sub-classified based on the “scalability factor”.1) If the scalability factor stays constant as you scale. This is called “linear scalability“.2) But chances are that some components may not scale as well as others. A scalability factor below 1.0 is called “sub-linear scalability“.3) Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called “supra-linear scalability“.4) If the application is not designed for scalability, its possible that things can actually get worse as it scales. This is called “negative scalability“.

Brian Zimmer, architect at travel startup Yapta, highlights some worst practices jeopardizing the growth and scalability of a system:* The Golden Hammer. Forcing a particular technology to work in ways it was not intended is sometimes counter-productive. Using a database to store key-value pairs is one example. Another example is using threads to program for concurrency.* Resource Abuse. Manage the availability of shared resources because when they fail, by definition, their failure is experienced pervasively rather than in isolation. For example, connection management to the database through a thread pool.* Big Ball of Mud. Failure to manage dependencies inhibits agility and scalability.* Everything or Something. In both code and application dependency management, the worst practice is not understanding the relationships and formulating a model to facilitate their management. Failure to enforce diligent control is a contributing scalability inhibiter.* Forgetting to check the time. To properly scale a system it is imperative to manage the time alloted for requests to be handled.* Hero Pattern. One popular solution to the operation issue is a Hero who can and often will manage the bulk of the operational needs. For a large system of many components this approach does not scale, yet it is one of the most frequently-deployed solutions.* Not automating. A system too dependent on human intervention, frequently the result of having a Hero, is dangerously exposed to issues of reproducibility and hit-by-a-bus syndrome.* Monitoring. Monitoring, like testing, is often one of the first items sacrificed when time is tight.

If we are seeing a sustained arrival rate of requests, greater than our system is capable of processing, then something has to give. Having the entire system degrade is not the ideal service we want to give our customers. A better approach would be to process transactions at our systems maximum possible throughput rate, while maintaining a good response time, and rejecting requests above this arrival rate. - http://mechanical-sympathy.blogspot.com.au[..]apply-back-pressure-when-overloaded.html

Consistent naming conventionsStandard terminologyUniform error responsesAttention to detail (that’s the love part for APIs)REST APIs that work with only 2 resources and use 4 HTTP verbs (oh, there is so much more to talk about here…)Avoid API design by way of method-driven approach – that leads you down a slippery slope- See more at: http://blogs.ancestry.com[..]are-like-parenting/#sthash.Ux1LHMNU.dpuf